진단의학의 핵심은 알고리즘에 있습니다. 의료기관들은 사전에 분석된 개인별 위험도에 대한 자료를 제공 받고 데이터 과학 기반의 의료서비스를 제공할 수 있습니다.
이에 탈로스는 국내 주요 중증질환 예방을 위해 해당 분야 전문의들의 통찰력과 연구를 통해 우수한 성능의 인공지능 학습모델을 개발하여 개인의 질병 인지율 상승과 검진 기관의 검진 효율성을 돕겠습니다.
With our team of top medical experts and professionals, our vision is to innovate the health care experience with medical artificial intelligence and spearhead the industry to create social and economic value.
Apparatus for Predicting Intracranial Aneurysm Using Retinal Fundus Image and Method for Providing Intracranial Aneurysm Prediction Results Using the Same
Diagnostic Aids System for Obstructive Sleep Apnea Using Cephaloradiographs and Method for Providing Diagnostic Aids thereof
Diagnostic Aids Method and Device for Cardioembolic Cerebral Infarction Using Chest Radiographs
Device and Method for Identifying Anatomical Location Using Flexible Bronchoscopy Images
Prediction of intracranial Aneurysm Risk using Machine Learning Scientific reports 10, no. 1 (2020): 1-10
Validation of prediction algorithm for risk estimation of intracranial aneurysm development using real-world data Scientific reports 13, (2023)
Incidence and risk factors of intracranial aneurysm: a national cohort study in Korea International Journal of Stroke 11, no. 8 (2016): 917-927
Machine learning for detecting moyamoya disease in plain skull radiography using a convolutional neural network EBioMedicine 40 (2019): 636-642
Stroke prevention by direct revascularization for patients with adult-onset moyamoya disease presenting with ischemia Journal of Neurosurgery 124, no. 6 (2016): 1788-1793